System architecture for universal emotive autography

ABSTRACT

A method of emotive autography includes calculating a plurality of classifiers associated with an individual user. Each of the classifiers indicates a preference of the user for an associated type of multimedia content. Multimedia data is received including video data, audio data and/or image data. The multimedia data is divided into semantically similar segments. A respective preference score is assigned to each of the semantically similar segments by use of the classifiers. The semantically similar segments are arranged in a sequential order dependent upon the preference scores. An emotive autograph is presented based on the semantically similar segments arranged in the sequential order.

BACKGROUND ART

Emotive autography is the practice of combining different types andmodes of information such as images, sound, and video to create contentwith a strong emotional component that reflects the content creatorand/or content consumer's preferences. For example, a set of videos andimages taken by a user during a holiday trip involving landmarks,beaches, and extreme sports can be intelligently combined or summarizedto produce a movie that emphasizes her preference for beaches, or thatemphasizes a preference (say, extreme sports) of the person(s) she wantsto share it with. In some cases, there is no practical way to tailorsuch movies to the preferences of an individual viewer because the samecontent understanding engine is used for all the users to determinetheir preferences as a group.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of a video summarization method;

FIG. 2 is an illustration of a distinction between emotive autographyand existing video summarization techniques;

FIG. 3 is a block diagram of a universal emotive autography arrangement;

FIG. 4A is a block diagram of a universal emotive autographyarrangement;

FIG. 4B is a process flow diagram of a method for performing emotiveautography;

FIG. 5 is a block diagram showing a tangible, non-transitorycomputer-readable medium that stores instructions for a universaluser-centric emotive autography arrangement;

FIG. 6 is an illustrative diagram of an example system, arranged inaccordance with at least some implementations of the present disclosure;and

FIG. 7 illustrates an example small form factor device, arranged inaccordance with at least some implementations of the present disclosure.

The same numbers are used throughout the disclosure and the figures toreference like components and features. Numbers in the 100 series referto features originally found in FIG. 1; numbers in the 200 series referto features originally found in FIG. 2; and so on.

DESCRIPTION OF THE ASPECTS

The present techniques relate generally to emotive autography based uponinputs from a user. Embodiments described herein enables emotiveautography where a plurality of classifiers associated with anindividual user are calculated. Each of the classifiers can indicate apreference of its respective user for an associated type of content. Thedata may be received, where the data includes video data, audio data,image data, other sensory data such as activity log from a wearablebio-sensor, and/or any combination thereof. The data may be divided intosemantically similar segments, and a respective preference score isassigned to each of the semantically similar segments by use of the userspecific classifiers. The semantically similar segments may be arrangedin a sequential order according to the preference scores, and an emotiveautograph may be presented based on the semantically similar segmentsarranged in the sequential order.

Some embodiments may be implemented in one or a combination of hardware,firmware, and software. Further, some embodiments may also beimplemented as instructions stored on a machine-readable medium, whichmay be read and executed by a computing platform to perform theoperations described herein. A machine-readable medium may include anymechanism for storing or transmitting information in a form readable bya machine, e.g., a computer. For example, a machine-readable medium mayinclude read only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; flash memory devices; orelectrical, optical, acoustical or other form of propagated signals,e.g., carrier waves, infrared signals, digital signals, or theinterfaces that transmit and/or receive signals, among others.

An embodiment is an implementation or example. Reference in thespecification to “an embodiment,” “one embodiment,” “some embodiments,”“various embodiments,” or “other embodiments” means that a particularfeature, structure, or characteristic described in connection with theembodiments is included in at least some embodiments, but notnecessarily all embodiments, of the present techniques. The variousappearances of “an embodiment,” “one embodiment,” or “some embodiments”are not necessarily all referring to the same embodiments. Elements oraspects from an embodiment can be combined with elements or aspects ofanother embodiment.

Not all components, features, structures, characteristics, etc.described and illustrated herein need be included in a particularembodiment or embodiments. If the specification states a component,feature, structure, or characteristic “may”, “might”, “can” or “could”be included, for example, that particular component, feature, structure,or characteristic is not required to be included. If the specificationor claim refers to “a” or “an” element, that does not mean there is onlyone of the element. If the specification or claims refer to “anadditional” element, that does not preclude there being more than one ofthe additional element.

It is to be noted that, although some embodiments have been described inreference to particular implementations, other implementations arepossible according to some embodiments. Additionally, the arrangementand/or order of circuit elements or other features illustrated in thedrawings and/or described herein need not be arranged in the particularway illustrated and described. Many other arrangements are possibleaccording to some embodiments.

In each system shown in a figure, the elements in some cases may eachhave a same reference number or a different reference number to suggestthat the elements represented could be different and/or similar.However, an element may be flexible enough to have differentimplementations and work with some or all of the systems shown ordescribed herein. The various elements shown in the figures may be thesame or different. Which one is referred to as a first element and whichis called a second element is arbitrary.

FIG. 1 is an illustration of a method 100 for video summarization. Inembodiments, video summarization is one of the usages that EmotiveAutography enables. Video or audio frames are analyzed and understoodusing a set of classifiers that remains the same across users and overtime (referred to as Semantic Knowledge Extractor/SKE; block 110). A bagof visual/aural/kinetic or multimodal classifiers may be used tounderstand frames or sets of frames.

At block 120, these subsampled key frames are scored based on SKEoutput, combined possibly with inertial measurement unit (IMU) sensoroutputs and certain interestingness/relevance parameters that may or maynot be user specific.

At block 140, a subset of top scored frames satisfying certainconstraints are selected. Key frames are selected based on the abovescores and certain constraints such as desired video length andconceptual smoothness (e.g., lack of abrupt changes) of the summarizedvideo.

At block 160, the key frames are blended, and external content such asbackground music may be added, to create a summary.

In embodiments, the present techniques fundamentally tie block 110 ofFIG. 1 to block 120 and make the whole framework user-centric. Users'preferences are fundamentally designed into the framework. Theclassifiers used to understand the video and audio frames are notgeneric in this case and are built specifically for the particular user.This significantly improves the accuracy of the classifiers and therebythe relevance of the selected key frames. In embodiments, reducing thenumber of classes improves the accuracy of the classifier.

The present techniques are evolved with regard to the semantic knowledgeextraction (SKE). Specifically, the approach of the present techniquesis inherently dynamic and evolves to a better summary over time as theframe-understanding piece (SKE) adapts and improves over time.Classifiers adapt to the user and improve over time. FIG. 2 is anillustration of a distinction between emotive autography and existingvideo summarization techniques. FIG. 2 includes a plurality ofclassifiers 200. As illustrated in FIG. 2, user 1's initial classifiersA1, B1, C1 (block 210) evolve to more accurate classifiers A1 x, B1 x,C1 x (block 220) at time x, and a new classifier D1 x is introduced tocapture new types of video that user 1 captured by this time x.Classifiers A1 x, B1 x, C1 x, D1 x evolve to more accurate classifiersA1 y, B1 y, C1 y, D1 y (block 230) at time y. User 2's initialclassifiers A2, B2, C2 evolve to more accurate classifiers A2 x, B2 x,C2 x at time x, and evolve to even more accurate classifiers A2 y, B2 y,C2 y at time y. A new classifier E2 y is introduced to capture new typesof video that user 2 captured by this time y. In contrast, in the priorart methods, the classifiers for both user 1 and user 2 are the same andremain frozen or constant across time. In addition to theabove-described features, the present disclosure may provide a userfeedback mechanism which alleviates the need for training datacollection.

In embodiments, the classifiers are adaptive. As described herein,adaptive classifiers are classifiers that are dynamic and can changeover time. As illustrated by FIG. 2, the classifiers are designed forthe particular users, and the classifiers evolve or are trained overtime. By contrast, traditional classifiers are frozen or constant. Thepresent techniques also enable a system architecture for universaluser-centric emotive autography which enforces the emotional componentto reflect the content creator and/or content consumer's preferences.For example, a set of videos and images taken by a user during a holidaytrip involving landmarks, beaches, and extreme sports can be summarizedto produce a movie that emphasizes her preference for beaches, or thatemphasizes a preference (e.g., extreme sports) of the person(s) shewants to share the movie with. This approach can also be seen aspreference-centric. In the following, a channel may refer to a classname of a set of semantically similar signals such as objects, scenes,events, activities. Therefore, channel classifier means a classifier forsuch a class of objects, scenes, events or activities respectively. Thesystem may adaptively build channel classifiers specific to a user. Inthis manner, scalability of channel classification is achieved byaggregating custom classifiers across users. The present disclosure mayemploy negative channels to exclude signals that are uninteresting to auser.

In embodiments, training data is prepared on the fly such that a priorset of training data (labeled or unlabeled) is not necessary. A metricreferred to as combinatorial edit entropy may be employed for measuringthe semantic similarity of signals. Moreover, the present techniques maybe directed to unsupervised, automatic or computer-implementedclustering of images, audios, videos, combination thereof, and othersignals (e.g., from a gyroscope, heartbeat monitor, or accelerometer) bysemantic similarity to thereby discover channels of the user's interests(e.g., objects, scenes, events), called ME channels, and adaptivelylearn classifiers for these channels. Instead of using an impreciseclassifier for large set of object/scene classes, user preference basedsets of precise classifiers are built for a much smaller set ofobject/scene classes per user, while collecting training data on thefly. This reduces the number of classes, thereby significantly improvingthe accuracy of the classifiers for any given user.

The learned channel classifiers for the user are used to create anautograph from one or more sets of signals that the user has. Further,the autograph can also be created based on a second user(s) (e.g., theactual consumer of the autograph) by using the second user's set ofchannel classifiers. A combinatorial metric to measure semanticsimilarity is also included in the present techniques.

In a concept discovery step, given a set of signals (e.g., videos,images, and audio) from a user, the signals are segmented and clusteredinto clusters of semantically similar signals. In one embodiment, thesegments are represented as a probability distribution over bag of words(e.g., textual, visual, aural) and as a node of a graph. An edge weightbetween two nodes is the combinatorial edit entropy (defined below) ofthe probability distribution representing the second node given thefirst node. A graph partitioning algorithm minimizes an average setdistance of nodes within a cluster while maximizing the distance fromall nodes outside the cluster, and is used for clustering. Each node mayrepresent the content of all or a portion of an image file, video file,audio file, or any of the signals (e.g., heartbeat, thermal image), forexample.

In general, the clustering can be done based on visualfeatures/descriptors such as histogram of oriented gradients (HOG),scale-invariant feature transform (SIFT), speeded up robust features(SURF), GIST, aural features/descriptors such as mel-frequency cepstralcoefficients (MFCC), or features/descriptors learned using neuralnetworks, and suitable distance metrics such as Euclidean distance orEdit distance using suitable clustering algorithms such as k-means,k-medoids or graph-partitioning. A generic object/scene classifiertrained for large number of classes (possibly inaccurate) may also beused optionally during the clustering step. This step produces a set ofclusters for a user. Each cluster may be referred to as an ME channelfor the user and may correspond to an object, scene, event or anothermeaningful set of semantically similar signals.

In a concept selection step, the user annotates her ME channels withsemantically meaningful tags and optionally indicates whether she likescertain ME channels, and whether she dislikes certain channels. If thesystem has prior information about user preferences (e.g., from theuser's activity on Facebook/Instagram/Pinterest/Youtube, etc.), thatinformation may be used instead of, or in addition to, this interactivestep. This step produces a set of “important” ME channels for a userwith respective importance scores as well as a set of negative channelsto impose exclusion of certain signals.

In a selective supervised training step, a set of classifiers aretrained for each of the “important” ME channels as well as for negativechannels discovered in the concept selection stage. The training datafor this purpose comes from the tagging of the clusters in the conceptselection stage. Alternatively, the training data may come from thecloud (e.g., an external training database). The supervised learningschemes to train these classifiers could be based on support vectormachines, nearest neighbors, neural networks etc. This step may producea set of updated classifiers for the user for each of her important MEchannels and negative channels. These classifiers specific to a user maybe trained for small number of classes (e.g., less than fifty classes).

In an emotive autograph generation step, when a new signal or set ofsignals (e.g., a new video or a set of videos) arrive for a user, it issegmented, optionally clustered, and each segment (cluster) isclassified using the channel classifiers for that user (important MEchannel classifiers as well as negative channel classifiers) and isassigned a score based on the user's importance score for the channelsas well as the channel classifiers' confidence scores. Alternatively,the channel classifiers for the intended consumer of the emotiveautograph can be used instead of, or in addition to, the user's channelclassifiers. Any segment classified as a negative channel is assigned alower score. Using these scores, a set of top segments are selected. Atemporal sequencer makes sure there is a time ordering to the signals inthese segments. The blender utilizes this time ordering and a stitchingalgorithm to create an emotive autograph out of the signals in these topsegments. One value of the vector may represent the time at which theother values of the vector were in effect. Optionally, the user tags thesegments and assigns an importance score which in turn can be used toaugment the training data and relearn the channel classifiers (or addclassifiers for new channels). Additionally, the blending step mayinvolve the user in the stitching process wherein the user arranges someof the segments. The system may use a machine learning algorithm tolearn the user's stitching preferences to be used in the blending stepin future autography sessions. The signals may not necessarily becontiguous.

In a collective channel intelligence and emotive exploration step, theuser-centric channel co-occurrence statistics may be combined across allthe users and augment a user's importance score beyond her own usingthis collective information. This essentially speeds up learning for auser by using knowledge from other users. Let us define collaborativesimilarity between two channels as the normalized value across the setof all users of how often the two channels fall into the “important MEchannels/negative channels of a user with close enough importancescores”. For each channel, then there is a set of other channels thatare close to the channel with respect to this similarity score. Inautograph generation stage, a new autograph creation from a set ofsignals then also uses these additional similar channels and additionalsimilar exclusion channels. This may be referred to as emotiveexploration.

In the present techniques, a metric for semantic similarity is alsodeveloped, referred to as combinatorial edit entropy. Signalfeatures/descriptors may not necessarily lie in a Euclidean space. Forexample, consider a three-dimension color space with basis elements{Blue, Pale Blue, Red}. These three basis elements are not orthogonal toone another. Consider three vectors in this space representing threerespective signals: V1=[1.0, 0, 0], V2=[0.5, 0.5, 0], V3=[0.5, 0, 0.5].As per Euclidean distance, V1 is at a same distance from V2 as from V3.However, intuitively, V1 should be more similar to V2 than V3 as Blue iscloser to Pale Blue than to Red. To capture such a notion of similarityin a non-Euclidean space, the present techniques provide this as themetric combinatorial edit entropy.

Given two discrete probability distributions P and Q on a space S,combinatorial edit entropy is defined as D_(CEE)(P∥Q) of P given Q asthe output of the following combinatorial algorithm:

Input: (i) a probability space S and a distance metric M: S×S→R⁺,essentially an outcome disparity matrix, e.g., for each pair of outcomes(s, t) a cost of observing t instead of s; and (ii) two discreteprobability distributions P and Q on S.

Output: D_(CEE)(P∥Q, M)ϵ[0, 1]

In general, this output may represent the weight assigned to an edgebetween two nodes, and may range between zero and one, for example. Thisoutput may represent the similarity between the two nodes. As anexample, an image node may be in the form of a vector with numericalvalues of the magnitude of each color component in the image (e.g., red,green, blue), and may have one or more additional numerical values thatare associated with the image but that are not descriptive of the imageitself, such as the heart rate of the user as she is viewing the image.In general, a vector may contain any number of variables associated withcontent, and the respective vectors of two nodes may be used to evaluatethe similarity between the two nodes. For example, the vector may have afirst variable value to describe the content of an image, video segmentor audio segment as well as second variable value associated with thefirst variable value, such as the time of day, GPS data to describe thelocation of the content, and variable values that describe the state ofthe user's body at the time, such as heartbeat, the direction in whichshe is looking, the direction in which she is leaning, or other bodilymovements.

   Algorithm: (i) Choose tolerance parameter δ ∈ E R⁺ and/or an integerL

 |S| and compute δ_(s,L) ← |M(s,t₁) - M(s,t_(L))|  Where t_(i)'s aredefined such that M(s,t₁)

 M(s,t₁₂)

 ··· M(s,t_(i))

 .. M(s,t_(|S|)) (δs,L ← min (δ, |M(s,t₁) - M(s,t_(L))|) in case δ isprovided directly Also, η_(s) ← t₁ m_(max) ← C * max _(s∈S), t_(∈S) M(s,t) for a predefined constant C > 1 (ii) D ← 0; Repeat the followinguntil ||Q||₁ = 0. u ← arg max _(s∈S) Q(s) v ← arg max _(s∈S) { P(s) :|M(u, s) − M(u, η_(u))| < δ_(u,L) } m_(v,u) ← min {Q(u), P(v)} ifm_(v,u) > 0    Q(u) ← Q(u) − m_(v,u)    P(v) ← P(v) − m_(v,u)    D ← D +m_(v,u) * M(v, u) if m_(v,u) = 0    D ← D + m_(max) * Q(u)    Q(u) ← 0(iii) D ← (D + m_(max) * || P ||₁)/C    output DThe tolerance parameter may specify the degree of similarity that valueshave in order to be in the same node.

With regard to dynamic versus static and repeatability requirements, theinventive emotive autography method is essentially dynamic and evolvesdue to on-the-fly training/learning versus a static method. This meansthat at equilibrium (e.g., after learning enough about the user),repeatability may be a requirement (i.e., getting the same autographgiven a particular input). However, in the short term, repeatability isnot guaranteed wherein a learning via another signal may happenin-between that may change the user's importance/preference scores.However, the autograph would arguably get better as the classifiersevolve.

In one embodiment, the user actively indicates her preferences, likesand dislikes, and these indications are used to create the emotiveautography. For example, the user may be presented with five to tenimages captured at approximately the same time and place, and the usermay indicate which of the images are important, or that she likes ordislikes in terms of the content of the images. The user's indicationsmay be included as variables in the node vectors.

FIG. 3 is a block diagram of a universal emotive autography arrangement300 of the present disclosure, including a concept discovery block 302,a concept selection block 304, a selective supervised learning block306, and classifiers 308. Visual data and multimedia data signals 310for each of n number of users is received by concept discovery block302.

In concept discovery block 302, multimedia data signals 310 aresegmented and clustered into clusters of semantically similar signals.The clustering may include unsupervised clustering 312 and/or supervisedweak classifier based clustering 314.

In a concept selection block 304, clusters or ME channels 316 for eachuser are received from concept discovery block 302. In block 318, theuser interactively selects or tags her clusters or ME channels withsemantically meaningful tags and optionally indicates whether she likesor dislikes certain clusters or ME channels. If the system has priorinformation about user preferences (e.g., from the user's selections and“likes” on Internet websites), then in block 320 that information may beadaptively used instead of, or in addition to, the user interactiveblock 318. Accordingly, block 304 produces a set of “important” or likedME channels 322 and a set of “negative” or unliked ME channels 324 foreach user.

In a selective supervised learning block 306, a set of classifiers aretrained for each of the liked channels and unliked channels. That is,liked ME channels 322 and unliked ME channels 324 are received for eachuser from concept selection block 304. The training data results fromthe tagging of the clusters in concept selection block 304. That is, asindicated at 326, the active selection of clusters by the user resultsin concept/object/scene/training data being received by a training dataselector 328. Training data selector 328 may produce training data forall the ME and negative channels for the users, and this data may beused by supervised learner 330. It is also possible for the trainingdata to be received from external sources on the Internet. Accordingly,selective supervised learning block 306 may produce a set of updatedclassifiers 308 for liked and unliked channels for each user.

FIG. 4A is a block diagram of another embodiment of a universal emotiveautography arrangement 400A of the present disclosure, including asegmentation block 402, a segments classification and scoring block 404,a segment sequencer block 406, an adaptive concept/object/scene learner408, and a blender 409. Visual data and multimedia data signals 410 froma particular user i are received by segmentation block 402. Visual dataand multimedia data signals 410 may be in the form of images, video andaudio files that the user has captured, recorded, downloaded orotherwise received on her smartphone. However, it is also possible forsignals 410 to be content that the user has not necessarily ever beforeseen or heard, such as online news content, motion pictures that mayhave been shown in movie theaters, or results of online searches forvideos or images. The user may have conducted such searches, or thesearches may have been performed automatically by the system. Signals410 may also be content that the user's friends have on their smartphoneor social media accounts that the user's friends have given the useraccess to.

In segmentation block 402, signals 410 are segmented and clustered intosegments 405 of semantically similar signals. For example, segments 405may be assigned one or more textual or alphanumeric tags, wherein eachtag describes the content of the segments. The content may be describedgenerally (e.g., indoor or outdoor), specifically (e.g., skydiving orbaseball), or any degree of specificity in-between. The content of twosignals may be semantically similar in a general sense (e.g., bothcontent is directed to the outdoors), or may be semantically similar ina more specific sense (e.g., both content is directed to skydiving).Segments 405 may be represented as a node of a graph. The nodes ofsegments 405 that are more semantically similar may be closer to eachother on the graph than are the nodes of segments 405 that are lesssemantically similar. A graph partitioning algorithm may be used forclustering segments 405 into clusters. Each segment 405 may representthe content of all or a portion of an image file, video file, audiofile, or any of the signals (e.g., heartbeat, accelerometer), forexample. A single video file, for example, may include different contentin different time periods, and the segments may begin and end when thedifferent content begins and ends. For example, a two-minute video mayhave indoor content in the first minute and outdoor content in thesecond minute, and thus the video may be divided into an indoor segmentand an outdoor segment, each of one minute duration.

In segments classification and scoring block 404, segments 405 areclassified and scored. Classifiers 414 for ME channels and classifiers416 for negative channels for the user are applied to segments 405 by asegment scorer 418 to produce a corresponding score for each segment405. The score may indicate how well the user would probably like, or beinterested in, each segment 405.

Each segment 405 and its corresponding score may be received by segmentsequencer block 406 from segments classification and scoring block 404.Using the scores, a user preferential/ME sequencer 420 selects a set oftop segments 405. A temporal sequencer 422 provides the time ordering tothe signals in segments 405.

The time-sequenced and preference-sequenced segments are received by andpresented to the user, as indicated at 423, and the user mayinteractively select the segments and tag them, as indicated at 424. Thesegment selections and tags may be received by an adaptiveconcept/object/scene learner 408 which updates classifiers and transmitsthe updated classifiers 426 to blocks 414 and 416.

The time-sequenced and preference-sequenced segments are received byblender 409. Blender 409 also receives the segment selections and tagsfrom the user, as indicated at 428. Blender 409 may use thetime-sequenced and preference-sequenced segments to automatically createan emotive autograph 430 out of the signals in these segments. Theblending may include automatic time sequencing of the segments, or theuser may decide the time sequencing of the segments.

Certain components in FIGS. 3 and 4A have been labeled hereinschematically as “blocks”. However, it is to be understood that each ofthe blocks may be embodied in the form of physical electroniccomponents, such as modules, circuits, etc.

FIG. 4B is a process flow diagram of a method 400B for performingemotive autography. In a first block 450, a plurality of classifiersassociated with an individual user are calculated. Each of theclassifiers indicates a preference of the user for an associated type ofmultimedia content. For example, the use of convolutional neuralnetworks is one known technique for calculating or constructingclassifiers based upon previously received data associated with theindividual user.

Multimedia data including video data, audio data and/or image data isreceived (block 452). That is, digitized video data, audio data and/orimage data may be input to a universal user-centric emotive autographyarrangement, such as those illustrated in FIGS. 3 and 4. For example,visual data and multimedia data signals 410 from a particular user maybe received by segmentation block 402.

The multimedia data is divided into semantically similar segments (block454). For example, in segmentation block 402, signals 410 are segmentedand clustered into segments 405 of semantically similar signals.

A respective preference score is assigned to each of the semanticallysimilar segments by use of the classifiers (block 456). For example, insegments classification and scoring block 404, segments 405 areclassified and scored.

The semantically similar segments are arranged in a sequential order.The arranging is performed dependent upon the preference scores (block458). For example, temporal sequencer 422 may provide the time orderingto the signals in segments 405.

An emotive autograph is presented based on the semantically similarsegments arranged in the sequential order (block 460). For example,blender 409 may use the time-sequenced segments to automatically createan emotive autograph 430 out of the signals in these segments, andautograph 430 may be presented to the user.

FIG. 5 is a block diagram showing a tangible, non-transitorycomputer-readable medium that stores instructions for a universaluser-centric emotive autography arrangement. The tangible,non-transitory computer-readable media 500 may be accessed by aprocessor 502 over a computer bus 504. Furthermore, the tangible,non-transitory computer-readable medium 500 may include code configuredto direct the processor 502 to perform the methods described herein.

The various software components discussed herein may be stored on one ormore tangible, non-transitory computer-readable media 500, as indicatedin FIG. 5. A segmentation module 506 may be configured to segmentmultimedia data, which may be divided into semantically similarsegments. A classification module 508 may be configured to generateclassifiers dependent upon a preference of the user for an associatedtype of content. A scoring module 510 may be configured to generate ascore for each segment. An arranging module 512 may be configured toarrange the segments dependent upon the preference scores. An adaptivelearning module 514 is configured to change the identified classifiersover time. In this manner, the classifiers may be adaptively specific toa user. In embodiments, an emotive autograph is presented to a userbased on the semantically similar segments arranged in the sequentialorder.

The block diagram of FIG. 5 is not intended to indicate that thetangible, non-transitory computer-readable media 500 is to include allof the components shown in FIG. 5. Further, the tangible, non-transitorycomputer-readable media 500 may include any number of additionalcomponents not shown in FIG. 5, depending on the details of the specificimplementation.

FIG. 6 is an illustrative diagram of an example system 600, arranged inaccordance with at least some implementations of the present disclosure.In various implementations, system 600 may be a media system althoughsystem 600 is not limited to this context. For example, system 600 maybe incorporated into a personal computer (PC), laptop computer,ultra-laptop computer, tablet, touch pad, portable computer, handheldcomputer, palmtop computer, personal digital assistant (PDA), cellulartelephone, combination cellular telephone/PDA, television, smart device(e.g., smart phone, smart tablet or smart television), mobile internetdevice (MID), messaging device, data communication device, cameras (e.g.point-and-shoot cameras, super-zoom cameras, digital single-lens reflex(DSLR) cameras), and so forth.

In various implementations, system 600 includes a platform 602 coupledto a display 620. Platform 602 may receive content from a content devicesuch as content services device(s) 630 or content delivery device(s) 640or other similar content sources. A navigation controller 650 includingone or more navigation features may be used to interact with, forexample, platform 602 and/or display 620. Each of these components isdescribed in greater detail below.

In various implementations, platform 602 may include any combination ofa chipset 605, processor 610, memory 612, antenna 613, storage 614,graphics subsystem 615, applications 616 and/or radio 618. Chipset 605may provide intercommunication among processor 610, memory 612, storage614, graphics subsystem 615, applications 616 and/or radio 618. Forexample, chipset 605 may include a storage adapter (not depicted)capable of providing intercommunication with storage 614.

Processor 610 may be implemented as a Complex Instruction Set Computer(CISC) or Reduced Instruction Set Computer (RISC) processors, x86instruction set compatible processors, multi-core, or any othermicroprocessor or central processing unit (CPU). In variousimplementations, processor 610 may be dual-core processor(s), dual-coremobile processor(s), and so forth.

Memory 612 may be implemented as a volatile memory device such as, butnot limited to, a Random Access Memory (RAM), Dynamic Random AccessMemory (DRAM), or Static RAM (SRAM). Storage 614 may be implemented as anon-volatile storage device such as, but not limited to, a magnetic diskdrive, optical disk drive, tape drive, an internal storage device, anattached storage device, flash memory, battery backed-up SDRAM(synchronous DRAM), and/or a network accessible storage device. Invarious implementations, storage 614 may include technology to increasethe storage performance enhanced protection for valuable digital mediawhen multiple hard drives are included, for example.

Graphics subsystem 615 may perform processing of images such as still orvideo for display. Graphics subsystem 615 may be a graphics processingunit (GPU) or a visual processing unit (VPU), for example. An analog ordigital interface may be used to communicatively couple graphicssubsystem 615 and display 620. For example, the interface may be any ofa High-Definition Multimedia Interface, DisplayPort, wireless HDMI,and/or wireless HD compliant techniques. Graphics subsystem 615 may beintegrated into processor 610 or chipset 605. In some implementations,graphics subsystem 615 may be a stand-alone device communicativelycoupled to chipset 605.

The graphics and/or video processing techniques described herein may beimplemented in various hardware architectures. For example, graphicsand/or video functionality may be integrated within a chipset.Alternatively, a discrete graphics and/or video processor may be used.As still another implementation, the graphics and/or video functions maybe provided by a general purpose processor, including a multi-coreprocessor. In further embodiments, the functions may be implemented in aconsumer electronics device.

Radio 618 may include one or more radios capable of transmitting andreceiving signals using various suitable wireless communicationstechniques. Such techniques may involve communications across one ormore wireless networks. Example wireless networks include (but are notlimited to) wireless local area networks (WLANs), wireless personal areanetworks (WPANs), wireless metropolitan area network (WMANs), cellularnetworks, and satellite networks. In communicating across such networks,radio 618 may operate in accordance with one or more applicablestandards in any version.

In various implementations, display 620 may include any television typemonitor or display. Display 620 may include, for example, a computerdisplay screen, touch screen display, video monitor, television-likedevice, and/or a television. Display 620 may be digital and/or analog.In various implementations, display 620 may be a holographic display.Also, display 620 may be a transparent surface that may receive a visualprojection. Such projections may convey various forms of information,images, and/or objects. For example, such projections may be a visualoverlay for a mobile augmented reality (MAR) application. Under thecontrol of one or more software applications 616, platform 602 maydisplay user interface 622 on display 620.

In various implementations, content services device(s) 630 may be hostedby any national, international and/or independent service and thusaccessible to platform 602 via the Internet, for example. Contentservices device(s) 630 may be coupled to platform 602 and/or to display620. Platform 602 and/or content services device(s) 630 may be coupledto a network 660 to communicate (e.g., send and/or receive) mediainformation to and from network 660. Content delivery device(s) 640 alsomay be coupled to platform 602 and/or to display 620.

In various implementations, content services device(s) 630 may include acable television box, personal computer, network, telephone, Internetenabled devices or appliance capable of delivering digital informationand/or content, and any other similar device capable ofuni-directionally or bi-directionally communicating content betweencontent providers and platform 602 and/display 620, via network 660 ordirectly. It will be appreciated that the content may be communicateduni-directionally and/or bi-directionally to and from any one of thecomponents in system 600 and a content provider via network 660.Examples of content may include any media information including, forexample, video, music, medical and gaming information, and so forth.

Content services device(s) 630 may receive content such as cabletelevision programming including media information, digital information,and/or other content. Examples of content providers may include anycable or satellite television or radio or Internet content providers.The provided examples are not meant to limit implementations inaccordance with the present disclosure in any way. In variousimplementations, platform 602 may receive control signals fromnavigation controller 650 having one or more navigation features. Thenavigation features of controller 650 may be used to interact with userinterface 622, for example. In various embodiments, navigationcontroller 650 may be a pointing device that may be a computer hardwarecomponent (specifically, a human interface device) that allows a user toinput spatial (e.g., continuous and multi-dimensional) data into acomputer. Many systems such as graphical user interfaces (GUI), andtelevisions and monitors allow the user to control and provide data tothe 6 computer or television using physical gestures.

Movements of the navigation features of controller 650 may be replicatedon a display (e.g., display 620) by movements of a pointer, cursor,focus ring, or other visual indicators displayed on the display. Forexample, under the control of software applications 616, the navigationfeatures located on navigation controller 650 may be mapped to virtualnavigation features displayed on user interface 622, for example. Invarious embodiments, controller 650 may not be a separate component butmay be integrated into platform 602 and/or display 620. The presentdisclosure, however, is not limited to the elements or in the contextshown or described herein.

In various implementations, drivers (not shown) may include technologyto enable users to instantly turn on and off platform 602 like atelevision with the touch of a button after initial boot-up, whenenabled, for example. Program logic may allow platform 602 to streamcontent to media adaptors or other content services device(s) 630 orcontent delivery device(s) 640 even when the platform is turned “off.”In addition, chipset 605 may include hardware and/or software supportfor 5.1 surround sound audio and/or high definition 6.1 surround soundaudio, for example. Drivers may include a graphics driver for integratedgraphics platforms. In various embodiments, the graphics driver maycomprise a peripheral component interconnect (PCI) Express graphicscard.

In various implementations, any one or more of the components shown insystem 600 may be integrated. For example, platform 602 and contentservices device(s) 630 may be integrated, or platform 602 and contentdelivery device(s) 640 may be integrated, or platform 602, contentservices device(s) 630, and content delivery device(s) 640 may beintegrated, for example. In various embodiments, platform 602 anddisplay 620 may be an integrated unit. Display 620 and content servicedevice(s) 630 may be integrated, or display 620 and content deliverydevice(s) 640 may be integrated, for example. These examples are notmeant to limit the present disclosure.

In various embodiments, system 600 may be implemented as a wirelesssystem, a wired system, or a combination of both. When implemented as awireless system, system 600 may include components and interfacessuitable for communicating over a wireless shared media, such as one ormore antennas, transmitters, receivers, transceivers, amplifiers,filters, control logic, and so forth. An example of wireless sharedmedia may include portions of a wireless spectrum, such as the RFspectrum and so forth. When implemented as a wired system, system 600may include components and interfaces suitable for communicating overwired communications media, such as input/output (I/O) adapters,physical connectors to connect the I/O adapter with a correspondingwired communications medium, a network interface card (NIC), disccontroller, video controller, audio controller, and the like. Examplesof wired communications media may include a wire, cable, metal leads,printed circuit board (PCB), backplane, switch fabric, semiconductormaterial, twisted-pair wire, co-axial cable, fiber optics, and so forth.

Platform 602 may establish one or more logical or physical channels tocommunicate information. The information may include media informationand control information. Media information may refer to any datarepresenting content meant for a user. Examples of content may include,for example, data from a voice conversation, videoconference, streamingvideo, electronic mail (“email”) message, voice mail message,alphanumeric symbols, graphics, image, video, text and so forth. Datafrom a voice conversation may be, for example, speech information,silence periods, background noise, comfort noise, tones and so forth.Control information may refer to any data representing commands,instructions or control words meant for an automated system. Forexample, control information may be used to route media informationthrough a system, or instruct a node to process the media information ina predetermined manner. The embodiments, however, are not limited to theelements or in the context shown or described in FIG. 6.

As described above, system 600 may be embodied in varying physicalstyles or form factors. FIG. 7 illustrates an example small form factordevice 700, arranged in accordance with at least some implementations ofthe present disclosure. In some examples, system 600 may be implementedvia device 700. In other examples, device 100 or portions thereof may beimplemented via device 700. In various embodiments, for example, device700 may be implemented as a mobile computing device a having wirelesscapabilities. A mobile computing device may refer to any device having aprocessing system and a mobile power source or supply, such as one ormore batteries, for example. While FIG. 7 illustrates a small formfactor device, the present techniques are not limited to small formfactors or mobile phones. The present techniques may be used with anyelectronic device of any size.

Examples of a mobile computing device may include a personal computer(PC), laptop computer, ultra-laptop computer, tablet, touch pad,portable computer, handheld computer, palmtop computer, personal digitalassistant (PDA), cellular telephone, combination cellular telephone/PDA,smart device (e.g., smart phone, smart tablet or smart mobiletelevision), mobile internet device (MID), messaging device, datacommunication device, cameras, and so forth.

Examples of a mobile computing device also may include computers thatare arranged to be worn by a person, such as a wrist computers, fingercomputers, ring computers, eyeglass computers, belt-clip computers,arm-band computers, shoe computers, clothing computers, and otherwearable computers. In various embodiments, for example, a mobilecomputing device may be implemented as a smart phone capable ofexecuting computer applications, as well as voice communications and/ordata communications. Although some embodiments may be described with amobile computing device implemented as a smart phone by way of example,it may be appreciated that other embodiments may be implemented usingother wireless mobile computing devices as well. The embodiments are notlimited in this context.

As shown in FIG. 7, device 700 may include a housing with a front 701and a back 702. Device 700 includes a display 704, an input/output (I/O)device 706, and an integrated antenna 708. Device 700 also may includenavigation features 712. I/O device 706 may include any suitable I/Odevice for entering information into a mobile computing device. Examplesfor I/O device 706 may include an alphanumeric keyboard, a numerickeypad, a touch pad, input keys, buttons, switches, microphones,speakers, voice recognition device and software, and so forth.Information also may be entered into device 700 by way of microphone(not shown), or may be digitized by a voice recognition device. Asshown, device 700 may include a camera 705 (e.g., including a lens, anaperture, and an imaging sensor) and a flash 710 integrated into back702 (or elsewhere) of device 700. In other examples, camera 705 andflash 710 may be integrated into front 701 of device 700 or both frontand back cameras may be provided. Camera 705 and flash 710 may becomponents of a camera module to originate image data processed intostreaming video that is output to display 704 and/or communicatedremotely from device 700 via antenna 708 for example.

Example 1 is a method of emotive autography, the method. The methodincludes calculating a plurality of classifiers associated with anindividual user, each of the classifiers indicating a preference of theuser for an associated type of content; receiving data; dividing thedata into semantically similar segments; assigning a respectivepreference score to each of the semantically similar segments by use ofthe classifiers; arranging the semantically similar segments in asequential order, the arranging being performed dependent upon thepreference scores; and presenting an emotive autograph based on thesemantically similar segments arranged in the sequential order.

Example 2 includes the method of example 1, including or excludingoptional features. In this example, the method includes the further stepof using the semantically similar segments arranged in the sequentialorder to update the classifiers. Optionally, the updating of theclassifiers includes receiving from the user selections of the segments.Optionally, the updating of the classifiers includes receiving from theuser tags that the user has assigned to the segments.

Example 3 includes the method of any one of examples 1 to 2, includingor excluding optional features. In this example, each of the classifiersquantifies the user's preferences regarding the content.

Example 4 includes the method of any one of examples 1 to 3, includingor excluding optional features. In this example, the data is receivedfrom the individual user.

Example 5 includes the method of any one of examples 1 to 4, includingor excluding optional features. In this example, the semanticallysimilar segments are arranged in the sequential order dependent uponrespective times at which the data was created.

Example 6 includes the method of any one of examples 1 to 5, includingor excluding optional features. In this example, first ones of theclassifiers are directed to content that the user likes and second onesof the classifiers are directed to content that the user dislikes.

Example 7 includes the method of any one of examples 1 to 6, includingor excluding optional features. In this example, each segment isrepresented by a node on a graph, the method further comprisingassigning weights to edges of the graph of nodes, each of the weightsindicating a degree of similarity between a corresponding pair of thenodes.

Example 8 includes the method of any one of examples 1 to 7, includingor excluding optional features. In this example, the content comprisesmultimedia or non-multimedia content.

Example 9 includes the method of any one of examples 1 to 8, includingor excluding optional features. In this example, the data comprisesvideo data, audio data, image data, data from sensors such asheart-rate, accelerometer, and global positioning system (GPS) sensors,or any combination thereof.

Example 10 is an emotive autography apparatus. The apparatus includes aselective supervised learning module configured to calculate a pluralityof classifiers associated with an individual user, each of theclassifiers indicating a preference of the user for an associated typeof content; a segmentation module configured to divide received datainto semantically similar segments; a segments classification andscoring module communicatively coupled to the segmentation module and tothe selective supervised learning module, the segments classificationand scoring module being configured to assign a respective preferencescore to each of the semantically similar segments by use of theclassifiers; a segment sequencer communicatively coupled to the segmentsclassification and scoring module and configured to arrange thesemantically similar segments in a sequential order, the arranging beingperformed dependent upon the preference scores; and a blender modulecommunicatively coupled to the segment sequencer and configured topresent an emotive autograph based on the semantically similar segmentsarranged in the sequential order.

Example 11 includes the apparatus of example 10, including or excludingoptional features. In this example, the apparatus includes an adaptivelearner module communicatively coupled to the segment sequencer andconfigured to use the semantically similar segments arranged in thesequential order to update the classifiers. Optionally, the adaptivelearner module is configured to receive from the user selections of thesegments and use the selections in the updating of the classifiers.Optionally, the adaptive learner module is configured to receive fromthe user tags that the user has assigned to the segments and use thetags in the updating of the classifiers.

Example 12 includes the apparatus of any one of examples 10 to 11,including or excluding optional features. In this example, each of theclassifiers quantifies the user's preferences regarding the content.

Example 13 includes the apparatus of any one of examples 10 to 12,including or excluding optional features. In this example, thesegmentation module is configured to receive the data from theindividual user.

Example 14 includes the apparatus of any one of examples 10 to 13,including or excluding optional features. In this example, the segmentsequencer is configured to arrange the semantically similar segments inthe sequential order dependent upon respective times at which themultimedia data was created.

Example 15 includes the apparatus of any one of examples 10 to 14,including or excluding optional features. In this example, first ones ofthe classifiers are directed to ME channels for the user and second onesof the classifiers are directed to negative channels for the user.

Example 16 includes the apparatus of any one of examples 10 to 15,including or excluding optional features. In this example, the segmentsclassification and scoring module is configured to: represent eachsegment by a respective node on a graph; and assign weights to edges ofthe graph of nodes, each of the weights indicating a degree ofsimilarity between a corresponding pair of the nodes.

Example 17 includes the apparatus of any one of examples 10 to 16,including or excluding optional features. In this example, the contentcomprises multimedia or non-multimedia content.

Example 18 includes the apparatus of any one of examples 10 to 17,including or excluding optional features. In this example, the datacomprises video data, audio data, image data, data from sensors such asheart-rate, accelerometer, and global positioning system (GPS) sensors,or any combination thereof.

Example 19 is a method of creating an emotive autograph, the method. Themethod includes receiving a plurality of sets of data comprising videodata, audio data, image data, or sensor data, each set of data beingassociated with a respective user; segmenting and clustering the datainto clusters of semantically similar segments; tagging each of theclusters with a semantically meaningful tag; training a respective setof classifiers for each of the clusters for each of the users, thetraining being performed by use of the semantically meaningful tags;assigning a respective preference score to each of the semanticallysimilar segments by use of the classifiers; arranging the semanticallysimilar segments in a sequential order, the arranging being performeddependent upon the preference scores; and creating a presentation basedon the semantically similar segments arranged in the sequential order.

Example 20 includes the method of example 19, including or excludingoptional features. In this example, measuring semantically similarsegments comprises: segmenting a plurality of data into a set of atleast two segments; representing the at least two segments by a firstvector and a second vector, respectively; assigning a first number and asecond number for at least one pair of one coordinate from the firstvector and one coordinate from the second vector, wherein the firstnumber is less than the value of the coordinates from the respectivepair; computing a third number by summing the product of the firstnumber and the second number for the at least one pair; computing thefirst number to minimize the third number for the at least one pair;representing the at least two segments by the respective nodes on agraph; and assigning weights to edges of the graph of nodes, the weightsbeing the computed minimum value of the third number for the at leasttwo pair.

Example 21 includes the method of any one of examples 19 to 20,including or excluding optional features. In this example, the methodincludes the further step of using the semantically similar segmentsarranged in the sequential order to update the classifiers. Optionally,the updating of the classifiers includes receiving from the userselections of the segments. Optionally, the updating of the classifiersincludes receiving from the user tags that the user has assigned to thesegments.

Example 22 includes the method of any one of examples 19 to 21,including or excluding optional features. In this example, each of theclassifiers quantifies a preference of the user for an associated typeof multimedia or non-multimedia content.

Example 23 includes the method of any one of examples 19 to 22,including or excluding optional features. In this example, the data isreceived from the individual users.

Example 24 includes the method of any one of examples 19 to 23,including or excluding optional features. In this example, thesemantically similar segments are arranged in the sequential orderdependent upon respective times at which the multimedia data wascreated.

Example 25 includes the method of any one of examples 19 to 24,including or excluding optional features. In this example, first ones ofthe classifiers are directed to content that the user likes and secondones of the classifiers are directed to content that the user dislikes.

Example 26 includes the method of any one of examples 19 to 25,including or excluding optional features. In this example, each segmentis represented by a node on a graph, the method further comprisingassigning weights to edges of the graph of nodes, each of the weightsindicating a degree of similarity between a corresponding pair of thenodes.

Example 27 is an emotive autography system. The system includes aconcept discovery module configured to: receive a plurality of sets ofdata, each set of data being associated with a respective user; segmentand cluster the data into clusters of semantically similar segments; aconcept selection module communicatively coupled to the conceptdiscovery module and configured to tag each of the clusters with asemantically meaningful tag; a selective supervised learning modulecommunicatively coupled to the concept selection module and configuredto train a respective set of classifiers for each of the clusters foreach of the users, the training being performed by use of thesemantically meaningful tags; a segments classification and scoringmodule communicatively coupled to the selective supervised learningmodule, the segments classification and scoring module being configuredto assign a respective preference score to each of the semanticallysimilar segments by use of the classifiers; a segment sequencercommunicatively coupled to the segments classification and scoringmodule and configured to arrange the semantically similar segments in asequential order, the arranging being performed dependent upon thepreference scores; and a blender module communicatively coupled to thesegment sequencer and configured to present an emotive autograph basedon the semantically similar segments arranged in the sequential order.

Example 28 includes the system of example 27, including or excludingoptional features. In this example, the system includes an adaptivelearner module communicatively coupled to the segment sequencer andconfigured to use the semantically similar segments arranged in thesequential order to update the classifiers. Optionally, the adaptivelearner module is configured to receive from the user selections of thesegments and use the selections in the updating of the classifiers.Optionally, the adaptive learner module is configured to receive fromthe user tags that the user has assigned to the segments and use thetags in the updating of the classifiers.

Example 29 includes the system of any one of examples 27 to 28,including or excluding optional features. In this example, each of theclassifiers quantifies the user's preferences regarding the content.

Example 30 includes the system of any one of examples 27 to 29,including or excluding optional features. In this example, the conceptdiscovery module is configured to receive the multimedia data from theindividual user.

Example 31 includes the system of any one of examples 27 to 30,including or excluding optional features. In this example, the segmentsequencer is configured to arrange the semantically similar segments inthe sequential order dependent upon respective times at which themultimedia data was created.

Example 32 includes the system of any one of examples 27 to 31,including or excluding optional features. In this example, first ones ofthe classifiers are directed to ME channels for the user and second onesof the classifiers are directed to negative channels for the user.

Example 33 includes the system of any one of examples 27 to 32,including or excluding optional features. In this example, the segmentsclassification and scoring module is configured to: represent eachsegment by a respective node on a graph; and assign weights to edges ofthe graph of nodes, each of the weights indicating a degree ofsimilarity between a corresponding pair of the nodes.

Example 34 is a tangible, non-transitory, computer-readable medium. Thecomputer-readable medium includes instructions that direct the processorto calculate a plurality of classifiers associated with an individualuser, each of the classifiers indicating a preference of the user for anassociated type of content; divide received data into semanticallysimilar segments; assign a respective preference score to each of thesemantically similar segments by use of the classifiers; arrange thesemantically similar segments in a sequential order, the arranging beingperformed dependent upon the preference scores; and present an emotiveautograph based on the semantically similar segments arranged in thesequential order.

Example 35 includes the computer-readable medium of example 34,including or excluding optional features. In this example, thecomputer-readable medium includes the further step of using thesemantically similar segments arranged in the sequential order to updatethe classifiers. Optionally, the updating of the classifiers includesreceiving from the user selections of the segments. Optionally, theupdating of the classifiers includes receiving from the user tags thatthe user has assigned to the segments.

Example 36 includes the computer-readable medium of any one of examples34 to 35, including or excluding optional features. In this example,each of the classifiers quantifies the user's preferences regarding thecontent.

Example 37 includes the computer-readable medium of any one of examples34 to 36, including or excluding optional features. In this example, thedata is received from the individual user.

Example 38 includes the computer-readable medium of any one of examples34 to 37, including or excluding optional features. In this example, thesemantically similar segments are arranged in the sequential orderdependent upon respective times at which the data was created.

Example 39 includes the computer-readable medium of any one of examples34 to 38, including or excluding optional features. In this example,first ones of the classifiers are directed to content that the userlikes and second ones of the classifiers are directed to content thatthe user dislikes.

Example 40 includes the computer-readable medium of any one of examples34 to 39, including or excluding optional features. In this example,each segment is represented by a node on a graph, the computer readablemedium further comprising assigning weights to edges of the graph ofnodes, each of the weights indicating a degree of similarity between acorresponding pair of the nodes.

Example 41 includes the computer-readable medium of any one of examples34 to 40, including or excluding optional features. In this example, thecontent comprises multimedia or non-multimedia content.

Example 42 includes the computer-readable medium of any one of examples34 to 41, including or excluding optional features. In this example, thedata comprises video data, audio data, image data, data from sensorssuch as heart-rate, accelerometer, and global positioning system (GPS)sensors, or any combination thereof.

Example 43 is an emotive autography apparatus. The apparatus includesinstructions that direct the processor to a plurality of means toindicate a preference of a user, wherein the preference is associatedwith a type of content; a segmentation module configured to dividereceived data into semantically similar segments; a segmentsclassification and scoring module communicatively coupled to thesegmentation module and to the plurality of means to indicate thepreference of a user, the segments classification and scoring modulebeing configured to assign a respective preference score to each of thesemantically similar segments by use of the plurality of means toindicate the preference of a user; a segment sequencer communicativelycoupled to the segments classification and scoring module and configuredto arrange the semantically similar segments in a sequential order, thearranging being performed dependent upon the preference scores; and ablender module communicatively coupled to the segment sequencer andconfigured to present an emotive autograph based on the semanticallysimilar segments arranged in the sequential order.

Example 44 includes the apparatus of example 43, including or excludingoptional features. In this example, the apparatus includes an adaptivelearner module communicatively coupled to the segment sequencer andconfigured to use the semantically similar segments arranged in thesequential order to update the plurality of means to indicate thepreference of the user. Optionally, the adaptive learner module isconfigured to receive from the user selections of the segments and usethe selections in the updating of the plurality of means to indicate thepreference of the user. Optionally, the adaptive learner module isconfigured to receive from the user tags that the user has assigned tothe segments and use the tags in the updating of the plurality of meansto indicate the preference of the user.

Example 45 includes the apparatus of any one of examples 43 to 44,including or excluding optional features. In this example, each of theplurality of means to indicate the preference of the user quantifies theuser's preferences regarding the content.

Example 46 includes the apparatus of any one of examples 43 to 45,including or excluding optional features. In this example, thesegmentation module is configured to receive the data from theindividual user.

Example 47 includes the apparatus of any one of examples 43 to 46,including or excluding optional features. In this example, the segmentsequencer is configured to arrange the semantically similar segments inthe sequential order dependent upon respective times at which themultimedia data was created.

Example 48 includes the apparatus of any one of examples 43 to 47,including or excluding optional features. In this example, first ones ofthe plurality of means to indicate the preference of the user aredirected to ME channels for the user and second ones of the plurality ofmeans to indicate the preference of the user are directed to negativechannels for the user.

Example 49 includes the apparatus of any one of examples 43 to 48,including or excluding optional features. In this example, the segmentsclassification and scoring module is configured to: represent eachsegment by a respective node on a graph; and assign weights to edges ofthe graph of nodes, each of the weights indicating a degree ofsimilarity between a corresponding pair of the nodes.

Example 50 includes the apparatus of any one of examples 43 to 49,including or excluding optional features. In this example, the contentcomprises multimedia or non-multimedia content.

Example 51 includes the apparatus of any one of examples 43 to 50,including or excluding optional features. In this example, the datacomprises video data, audio data, image data, data from sensors such asheart-rate, accelerometer, and global positioning system (GPS) sensors,or any combination thereof.

It is to be understood that specifics in the aforementioned examples maybe used anywhere in one or more aspects. For instance, all optionalfeatures of the computing device described above may also be implementedwith respect to either of the methods or the computer-readable mediumdescribed herein. Furthermore, although flow diagrams and/or statediagrams may have been used herein to describe aspects, the techniquesare not limited to those diagrams or to corresponding descriptionsherein. For example, flow need not move through each illustrated box orstate or in exactly the same order as illustrated and described herein.

The present techniques are not restricted to the particular detailslisted herein. Indeed, those skilled in the art having the benefit ofthis disclosure will appreciate that many other variations from theforegoing description and drawings may be made within the scope of thepresent techniques. Accordingly, it is the following claims includingany amendments thereto that define the scope of the present techniques.

What is claimed is:
 1. A method of emotive autography, the methodcomprising: calculating a plurality of classifiers associated with anindividual user, each of the classifiers indicating a preference of theuser for an associated type of content; receiving data; dividing thedata into semantically similar segments; assigning a respectivepreference score to each of the semantically similar segments by use ofthe classifiers; arranging the semantically similar segments in asequential order, the arranging being performed dependent upon thepreference scores; and presenting an emotive autograph based on thesemantically similar segments arranged in the sequential order.
 2. Themethod of claim 1, comprising the further step of using the semanticallysimilar segments arranged in the sequential order to update theclassifiers.
 3. The method of claim 1, wherein an update of theclassifiers includes receiving from the user selections of the segments.4. The method of claim 1, wherein an update of the classifiers includesreceiving from the user tags that the user has assigned to the segments.5. The method of claim 1, wherein each of the classifiers quantifies theuser's preferences regarding the content.
 6. The method of claim 1,wherein the data is received from the individual user.
 7. The method ofclaim 1, wherein the semantically similar segments are arranged in thesequential order dependent upon respective times at which the data wascreated.
 8. The method of claim 1, wherein first ones of the classifiersare directed to content that the user likes and second ones of theclassifiers are directed to content that the user dislikes.
 9. Themethod of claim 1, wherein each segment is represented by a node on agraph, the method further comprising assigning weights to edges of thegraph of nodes, each of the weights indicating a degree of similaritybetween a corresponding pair of the nodes.
 10. The method of claim 1,wherein the content comprises multimedia or non-multimedia content. 11.The method of claim 1, wherein the data comprises video data, audiodata, image data, data from sensors including heart-rate, accelerometer,or global positioning system (GPS) sensors, or any combination thereof.12. An emotive autography apparatus, comprising: a processor configuredto execute: a selective supervised learning module configured tocalculate a plurality of classifiers associated with an individual user,each of the classifiers indicating a preference of the user for anassociated type of content; a segmentation module configured to dividereceived data into semantically similar segments; a segmentsclassification and scoring module communicatively coupled to thesegmentation module and to the selective supervised learning module, thesegments classification and scoring module being configured to assign arespective preference score to each of the semantically similar segmentsby use of the classifiers; a segment sequencer communicatively coupledto the segments classification and scoring module and configured toarrange the semantically similar segments in a sequential order, thearranging being performed dependent upon the preference scores; and ablender module communicatively coupled to the segment sequencer andconfigured to present an emotive autograph based on the semanticallysimilar segments arranged in the sequential order.
 13. The apparatus ofclaim 12, further comprising an adaptive learner module communicativelycoupled to the segment sequencer and configured to use the semanticallysimilar segments arranged in the sequential order to update theclassifiers.
 14. The apparatus of claim 12, wherein an adaptive learnermodule is configured to receive from the user selections of the segmentsand use the selections in the updating of the classifiers.
 15. Theapparatus of claim 12, wherein an adaptive learner module is configuredto receive from the user tags that the user has assigned to the segmentsand use the tags in the updating of the classifiers.
 16. The apparatusof claim 12, wherein each of the classifiers quantifies the user'spreferences regarding the content.
 17. An emotive autography system,comprising: a processor configured to execute: a concept discoverymodule configured to: receive a plurality of sets of data, each set ofdata being associated with a respective user; segment and cluster thedata into clusters of semantically similar segments; a concept selectionmodule communicatively coupled to the concept discovery module andconfigured to tag each of the clusters with a semantically meaningfultag; a selective supervised learning module communicatively coupled tothe concept selection module and configured to train a respective set ofclassifiers for each of the clusters for each of the users, the trainingbeing performed by use of the semantically meaningful tags; a segmentsclassification and scoring module communicatively coupled to theselective supervised learning module, the segments classification andscoring module being configured to assign a respective preference scoreto each of the semantically similar segments by use of the classifiers;a segment sequencer communicatively coupled to the segmentsclassification and scoring module and configured to arrange thesemantically similar segments in a sequential order, the arranging beingperformed dependent upon the preference scores; and a blender modulecommunicatively coupled to the segment sequencer and configured topresent an emotive autograph based on the semantically similar segmentsarranged in the sequential order.
 18. The system of claim 17, whereinthe concept discovery module is configured to receive the multimediadata from the individual user.
 19. The system of claim 17, wherein thesegment sequencer is configured to arrange the semantically similarsegments in the sequential order dependent upon respective times atwhich the multimedia data was created.
 20. The system of claim 17,wherein first ones of the classifiers are directed to ME channels forthe user and second ones of the classifiers are directed to negativechannels for the user.
 21. The system of claim 17, wherein the segmentsclassification and scoring module is configured to: represent eachsegment by a respective node on a graph; and assign weights to edges ofthe graph of nodes, each of the weights indicating a degree ofsimilarity between a corresponding pair of the nodes.
 22. A tangible,non-transitory, computer-readable medium comprising instructions that,when executed by a processor, direct the processor to: calculate aplurality of classifiers associated with an individual user, each of theclassifiers indicating a preference of the user for an associated typeof content; divide received data into semantically similar segments;assign a respective preference score to each of the semantically similarsegments by use of the classifiers; arrange the semantically similarsegments in a sequential order, the arranging being performed dependentupon the preference scores; and present an emotive autograph based onthe semantically similar segments arranged in the sequential order. 23.The computer readable medium of claim 22, wherein each of theclassifiers quantifies the user's preferences regarding the content. 24.The computer readable medium of claim 22, wherein the data is receivedfrom the individual user.
 25. The computer readable medium of claim 22,wherein the semantically similar segments are arranged in the sequentialorder dependent upon respective times at which the data was created.